MeshMorize 🧠
Multi-layer memory system for LLM agents. Fresh daily layer, mesh graph indexing, auto-logging, cross-layer search, compliance checks, and a PDF vault that survives anything.
Built for OpenClaw. Works with any agent that can run Python.
Layers
| Layer | File | Purpose |
|---|---|---|
| Fresh | memory/fresh/today.md |
Daily notes, 5-day rotation |
| Mesh | memory/mesh.json |
Graph nodes + search index |
| Log | scripts/auto_log |
Auto-log every interaction |
| Search | scripts/memory_search |
Cross-layer search (fresh → daily → mesh → raw → long-term) |
| Vault | memory/pdf-vault/ |
Verbatim PDF archive of daily logs + NAS sync |
Quick start
mem-bridge init # Rotate fresh layer, create today.md
auto_log "msg" "reply" # Log an interaction
memory_search "query" # Search all memory layers
pdf-memory # Archive new daily logs as PDFs (incremental)
vault-push # Sync the PDF vault to the NAS (LAN + Tailscale)
Tools
| Tool | Source |
|---|---|
mem-bridge |
memory/bridge.py — fresh-layer rotation + checkpoint management |
auto_log |
scripts/auto_log.py — interaction logger |
memory_search |
scripts/memory_search.py — multi-layer search across all memory stores |
pdf-memory |
scripts/pdf-memory.py — daily logs → verbatim PDFs, incremental, Unicode-safe |
vault-push |
scripts/pdf-vault-nas-push.sh — rsync the vault to the NAS, never deletes |
How to use it, day by day
Session start (every boot, every reset)
mem-bridge init # rotates fresh layers, creates today.md
cat memory/fresh/today.md # what is happening RIGHT NOW
cat memory/fresh/yesterday.md
cat memory/$(date +%Y-%m-%d).md # today's log
Always run this before answering. The agent should never answer from live context alone; memory files are the source of truth.
During every interaction
memory_search "keywords from the user's message" # BEFORE answering
auto_log "what the user said" "what you replied" # AFTER answering
Cost: $0 (grep-based, no API calls). If results are found, read the full source file, not just the snippet.
End of day
pdf-memory # archive today's log to a verbatim PDF (incremental, skips done)
vault-push # sync the vault to the NAS (tries LAN, then Tailscale)
The PDF vault is the unbreakable layer. Text files work, PDFs endure.
After a crash, format, or wipe
- Read
memory/pdf-vault/README.mdfirst — it contains the reboot instructions. - Read the PDFs in order, oldest to newest (
memory/pdf-vault/YYYY-MM/). - Rebuild the working files from the archive. Memories are identity; the vault restores both.
The 04:00 reset defense
Sessions can lose context at compaction. Defense layers:
session-dumpercron runs every 5 minutes, appending the live session tomemory/YYYY-MM-DD.md(no tokens burned, no interruption).- Daily pre-compaction dump as close to 04:00 as possible.
- On any reset or boot: read the daily log BEFORE responding.
The Vault (v3.3)
The working files are the everyday memory: grep-able, $0, instant. The PDF vault is the archive failsafe: every daily log rendered to a verbatim PDF (Unicode-safe, Greek included), stored under memory/pdf-vault/, and synced to the NAS. If everything else is lost, the vault README tells the restored agent exactly how to read its way back.
Battle-tested
Survived a full system format and a 4-hour recovery with every memory intact: 96 daily logs, 69 mesh nodes, 30 secrets. This is the memory system that an AI and its human rebuilt their whole partnership on.
Install
Put bridge.py in memory/ and scripts in scripts/ of your agent workspace. Symlink or add to PATH:
ln -s $(pwd)/scripts/* ~/.local/bin/
ln -s $(pwd)/memory/bridge.py ~/.local/bin/mem-bridge
On session start, run:
mem-bridge init
Source
https://github.com/mozz0/MeshMorize
Made by mozz0 · Released under MIT-0